Semiparametric inference for an accelerated failure time model with dependent truncation

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题名:	Semiparametric inference for an accelerated failure time model with dependent truncation
作者:	江村剛志;Emura, Takeshi;Wang, Weijing
贡献者:	理學院統計研究所
关键词:	Astronomy;Asymptotic properties;Censored data (mathematics);Econometrics;Economics;Epidemiology;Estimating;Estimation;Estimators;Failure times;Finance;Inference;Insurance;Linear programming;Management;Mathematical models;Mathematics;Mathematics and Statistics;Regression;Statistical analysis;Statistical models;Statistical tests;Statistics;Statistics for Business;Studies;Survival analysis;Variables
日期:	2016-10-01
上传时间:	2026-04-23 13:00:15 (UTC+8)
出版者:	Springer Netherlands;Tokyo: Springer Japan
摘要:	摘要： Truncated data are commonly seen in studies of biomedicine, epidemiology, astronomy and econometrics. Existing regression methods for analyzing left-truncated and right-censored data have been developed under the assumption that the lifetime variable of interest is independent of both truncation and censoring variables. In this article, we propose a semiparametric accelerated failure time model that incorporates both covariates and the truncation variable as regressors. The proposed model utilizes the truncation information in statistical modeling and hence allows for dependent truncation. For estimation, we develop a set of estimating equations constructed from the log-rank and quasi-independence test statistics. We show that the resulting estimators are consistent and asymptotically normal. We also propose an explicit formula for variance estimation based on a kernel method. Finite-sample performances of the estimators are studied by simulations. The proposed methodology is applied to analyze a real data for illustration. 其他題名： Ann Inst Stat Math 出版者： Tokyo: Springer Japan 出版日期： 2016-10-01 出處： Annals of the Institute of Statistical Mathematics, 2016-10, Vol.68 (5), p.1073-1094 資源來源： ABI/INFORM Collection (via ProQuest) 版權： The Institute of Statistical Mathematics, Tokyo 2015 版權： The Institute of Statistical Mathematics, Tokyo 2016 識別號： ISSN: 0020-3157 識別號： EISSN: 1572-9052 識別號： DOI: 10.1007/s10463-015-0526-9
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